@inproceedings{2927a7f9295d437086da856b726a73a5,
title = "A Deep Learning Network based Robust Fault Diagnosis Method for IGBT Open Circuit",
abstract = "This paper proposes an IGBT open-circuit fault diagnosis method that can maintain high accuracy under diverse operation conditions and circuit parameter variances. Different aspects of uncertainties are analyzed in the component parameters, operation conditions, and measurement errors of a three-phase inverter case study. A lightweight Convolutional Neural Network (CNN) is applied based on an obtained dataset covering a wide range of inverter operation scenarios and uncertainties. The comparisons with benchmarked conventional fault diagnosis method and with different machine learning methods are presented. The results verify the improved accuracy in open-circuit diagnosis considering complex operation conditions and meanwhile with reduced detection time in certain scenarios.",
keywords = "dynamic operation conditions, IGBT open circuit fault diagnosis, lightweight convolutional neural network, robustness",
author = "Yongjie Liu and Ariya Sangwongwanich and Yi Zhang and Rui Kong and Yingzhou Peng and Hosani, {Khalifa Al} and Huai Wang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia ; Conference date: 17-05-2024 Through 20-05-2024",
year = "2024",
doi = "10.1109/IPEMC-ECCEAsia60879.2024.10567329",
language = "British English",
series = "2024 IEEE 10th International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2366--2371",
booktitle = "2024 IEEE 10th International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia",
address = "United States",
}